# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Optional, Tuple, Union, Dict, Any
import torch
import torch.nn as nn
import torch_npu

from diffusers.models.embeddings import (
    Timesteps,
    TimestepEmbedding,
    TextImageProjection,
    ImageProjection,
    TextTimeEmbedding,
    TextImageTimeEmbedding,
    ImageTimeEmbedding,
    ImageHintTimeEmbedding
)

from diffusers.models.activations import get_activation
from diffusers.models.modeling_utils import ModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
from diffusers.loaders.single_file_model import FromOriginalModelMixin
from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, scale_lora_layers, unscale_lora_layers

from .unet_blocks import (
    get_down_block,
    get_mid_block,
    get_up_block
)


@dataclass
class UNet2DConditionOutput(BaseOutput):
    """
    The output of [`UNet2DConditionModel`].

    Args:
        sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
    """

    sample: torch.Tensor = None


class UNet2DConditionModel(
    ModelMixin, ConfigMixin, FromOriginalModelMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin
):
    r"""
    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
    shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample.
        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
            The tuple of upsample blocks to use.
        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
            Whether to include self-attention in the basic transformer blocks, see
            [`~models.attention.BasicTransformerBlock`].
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
            If `None`, normalization and activation layers is skipped in post-processing.
        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
            The dimension of the cross attention features.
        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
            [`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
        reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
            blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
            [`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
        encoder_hid_dim (`int`, *optional*, defaults to None):
            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
            dimension to `cross_attention_dim`.
        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
        num_attention_heads (`int`, *optional*):
            The number of attention heads. If not defined, defaults to `attention_head_dim`
        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
        addition_embed_type (`str`, *optional*, defaults to `None`):
            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
            "text". "text" will use the `TextTimeEmbedding` layer.
        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
            Dimension for the timestep embeddings.
        num_class_embeds (`int`, *optional*, defaults to `None`):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
            class conditioning with `class_embed_type` equal to `None`.
        time_embedding_type (`str`, *optional*, defaults to `positional`):
            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
        time_embedding_dim (`int`, *optional*, defaults to `None`):
            An optional override for the dimension of the projected time embedding.
        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
            Optional activation function to use only once on the time embeddings before they are passed to the rest of
            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
        timestep_post_act (`str`, *optional*, defaults to `None`):
            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
            The dimension of `cond_proj` layer in the timestep embedding.
        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
            embeddings with the class embeddings.
        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
            otherwise.
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D", "CrossAttnUpBlock2D"]

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 4,
        out_channels: int = 4,
        center_input_sample: bool = False,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
        only_cross_attention: Union[bool, Tuple[bool]] = False,
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        layers_per_block: Union[int, Tuple[int]] = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        dropout: float = 0.0,
        act_fn: str = "silu",
        norm_num_groups: Optional[int] = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: Union[int, Tuple[int]] = 1280,
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: Optional[str] = None,
        attention_head_dim: Union[int, Tuple[int]] = 8,
        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        class_embed_type: Optional[str] = None,
        addition_embed_type: Optional[str] = None,
        addition_time_embed_dim: Optional[int] = None,
        num_class_embeds: Optional[int] = None,
        upcast_attention: bool = False,
        resnet_time_scale_shift: str = "default",
        resnet_skip_time_act: bool = False,
        resnet_out_scale_factor: float = 1.0,
        time_embedding_type: str = "positional",
        time_embedding_dim: Optional[int] = None,
        time_embedding_act_fn: Optional[str] = None,
        timestep_post_act: Optional[str] = None,
        time_cond_proj_dim: Optional[int] = None,
        conv_in_kernel: int = 3,
        conv_out_kernel: int = 3,
        projection_class_embeddings_input_dim: Optional[int] = None,
        attention_type: str = "default",
        class_embeddings_concat: bool = False,
        mid_block_only_cross_attention: Optional[bool] = None,
        cross_attention_norm: Optional[str] = None,
        addition_embed_type_num_heads: int = 64,
        cache_method: str = None,
    ):
        super().__init__()

        self.sample_size = sample_size

        if num_attention_heads is not None:
            raise ValueError(
                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
            )

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        num_attention_heads = num_attention_heads or attention_head_dim

        # Check inputs
        self._check_config(
            down_block_types=down_block_types,
            up_block_types=up_block_types,
            only_cross_attention=only_cross_attention,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            cross_attention_dim=cross_attention_dim,
            transformer_layers_per_block=transformer_layers_per_block,
            reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
            attention_head_dim=attention_head_dim,
            num_attention_heads=num_attention_heads,
        )

        # input
        conv_in_padding = (conv_in_kernel - 1) // 2
        self.conv_in = nn.Conv2d(
            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
        )

        # time
        time_embed_dim, timestep_input_dim = self._set_time_proj(
            time_embedding_type,
            block_out_channels=block_out_channels,
            flip_sin_to_cos=flip_sin_to_cos,
            freq_shift=freq_shift,
            time_embedding_dim=time_embedding_dim,
        )

        self.time_embedding = TimestepEmbedding(
            timestep_input_dim,
            time_embed_dim,
            act_fn=act_fn,
            post_act_fn=timestep_post_act,
            cond_proj_dim=time_cond_proj_dim,
        )

        self._set_encoder_hid_proj(
            encoder_hid_dim_type,
            cross_attention_dim=cross_attention_dim,
            encoder_hid_dim=encoder_hid_dim,
        )

        # class embedding
        self._set_class_embedding(
            class_embed_type,
            act_fn=act_fn,
            num_class_embeds=num_class_embeds,
            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
            time_embed_dim=time_embed_dim,
            timestep_input_dim=timestep_input_dim,
        )

        self._set_add_embedding(
            addition_embed_type,
            addition_embed_type_num_heads=addition_embed_type_num_heads,
            addition_time_embed_dim=addition_time_embed_dim,
            cross_attention_dim=cross_attention_dim,
            encoder_hid_dim=encoder_hid_dim,
            flip_sin_to_cos=flip_sin_to_cos,
            freq_shift=freq_shift,
            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
            time_embed_dim=time_embed_dim,
        )

        if time_embedding_act_fn is None:
            self.time_embed_act = None
        else:
            self.time_embed_act = get_activation(time_embedding_act_fn)

        self.down_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        if isinstance(only_cross_attention, bool):
            if mid_block_only_cross_attention is None:
                mid_block_only_cross_attention = only_cross_attention

            only_cross_attention = [only_cross_attention] * len(down_block_types)

        if mid_block_only_cross_attention is None:
            mid_block_only_cross_attention = False

        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(down_block_types)

        if isinstance(attention_head_dim, int):
            attention_head_dim = (attention_head_dim,) * len(down_block_types)

        if isinstance(cross_attention_dim, int):
            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)

        if isinstance(layers_per_block, int):
            layers_per_block = [layers_per_block] * len(down_block_types)

        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)

        if class_embeddings_concat:
            # The time embeddings are concatenated with the class embeddings. The dimension of the
            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
            # regular time embeddings
            blocks_time_embed_dim = time_embed_dim * 2
        else:
            blocks_time_embed_dim = time_embed_dim

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block[i],
                transformer_layers_per_block=transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=blocks_time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim[i],
                num_attention_heads=num_attention_heads[i],
                downsample_padding=downsample_padding,
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,
                attention_type=attention_type,
                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
                cross_attention_norm=cross_attention_norm,
                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
                dropout=dropout,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = get_mid_block(
            mid_block_type,
            temb_channels=blocks_time_embed_dim,
            in_channels=block_out_channels[-1],
            resnet_eps=norm_eps,
            resnet_act_fn=act_fn,
            resnet_groups=norm_num_groups,
            output_scale_factor=mid_block_scale_factor,
            transformer_layers_per_block=transformer_layers_per_block[-1],
            num_attention_heads=num_attention_heads[-1],
            cross_attention_dim=cross_attention_dim[-1],
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            mid_block_only_cross_attention=mid_block_only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_type=attention_type,
            resnet_skip_time_act=resnet_skip_time_act,
            cross_attention_norm=cross_attention_norm,
            attention_head_dim=attention_head_dim[-1],
            dropout=dropout,
        )

        # count how many layers upsample the images
        self.num_upsamplers = 0

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        reversed_num_attention_heads = list(reversed(num_attention_heads))
        reversed_layers_per_block = list(reversed(layers_per_block))
        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
        reversed_transformer_layers_per_block = (
            list(reversed(transformer_layers_per_block))
            if reverse_transformer_layers_per_block is None
            else reverse_transformer_layers_per_block
        )
        only_cross_attention = list(reversed(only_cross_attention))

        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            is_final_block = i == len(block_out_channels) - 1

            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            # add upsample block for all BUT final layer
            if not is_final_block:
                add_upsample = True
                self.num_upsamplers += 1
            else:
                add_upsample = False

            up_block = get_up_block(
                up_block_type,
                num_layers=reversed_layers_per_block[i] + 1,
                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=blocks_time_embed_dim,
                add_upsample=add_upsample,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resolution_idx=i,
                resnet_groups=norm_num_groups,
                cross_attention_dim=reversed_cross_attention_dim[i],
                num_attention_heads=reversed_num_attention_heads[i],
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,
                attention_type=attention_type,
                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
                cross_attention_norm=cross_attention_norm,
                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
                dropout=dropout,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        if norm_num_groups is not None:
            self.conv_norm_out = nn.GroupNorm(
                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
            )

            self.conv_act = get_activation(act_fn)

        else:
            self.conv_norm_out = None
            self.conv_act = None

        conv_out_padding = (conv_out_kernel - 1) // 2
        self.conv_out = nn.Conv2d(
            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
        )

        self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim)
        
        def enable_agb_cache(module):
            for child in module.children():
                if hasattr(child, "enable_agb"):
                    child.enable_agb = True
                if len(list(child.children())) > 0:
                    enable_agb_cache(child)

        if cache_method == "agb_cache":
            enable_agb_cache(self.down_blocks)
            enable_agb_cache(self.mid_block)
            enable_agb_cache(self.up_blocks)
        self.enable_unet_cache = cache_method == "static_cache"
        self.cache = None
        self.cache_step = [1, 2, 4, 6, 7, 9, 10, 12, 13, 14, 16, 18, 19, 21, 23, 24, 26, 27, 29, \
            30, 31, 33, 34, 36, 37, 39, 40, 42, 43, 45, 47, 48, 49]
    
    def _check_config(
        self,
        down_block_types: Tuple[str],
        up_block_types: Tuple[str],
        only_cross_attention: Union[bool, Tuple[bool]],
        block_out_channels: Tuple[int],
        layers_per_block: Union[int, Tuple[int]],
        cross_attention_dim: Union[int, Tuple[int]],
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],
        reverse_transformer_layers_per_block: bool,
        attention_head_dim: int,
        num_attention_heads: Optional[Union[int, Tuple[int]]],
    ):
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
            )

        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
            )
        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
            for layer_number_per_block in transformer_layers_per_block:
                if isinstance(layer_number_per_block, list):
                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")

    def _set_time_proj(
        self,
        time_embedding_type: str,
        block_out_channels: int,
        flip_sin_to_cos: bool,
        freq_shift: float,
        time_embedding_dim: int,
    ) -> Tuple[int, int]:
        if time_embedding_type == "fourier":
            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
            if time_embed_dim % 2 != 0:
                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
            self.time_proj = GaussianFourierProjection(
                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
            )
            timestep_input_dim = time_embed_dim
        elif time_embedding_type == "positional":
            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4

            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
            timestep_input_dim = block_out_channels[0]
        else:
            raise ValueError(
                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
            )

        return time_embed_dim, timestep_input_dim

    def _set_encoder_hid_proj(
        self,
        encoder_hid_dim_type: Optional[str],
        cross_attention_dim: Union[int, Tuple[int]],
        encoder_hid_dim: Optional[int],
    ):
        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
            encoder_hid_dim_type = "text_proj"
            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")

        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
            raise ValueError(
                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
            )

        if encoder_hid_dim_type == "text_proj":
            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
        elif encoder_hid_dim_type == "text_image_proj":
            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)`
            self.encoder_hid_proj = TextImageProjection(
                text_embed_dim=encoder_hid_dim,
                image_embed_dim=cross_attention_dim,
                cross_attention_dim=cross_attention_dim,
            )
        elif encoder_hid_dim_type == "image_proj":
            # Kandinsky 2.2
            self.encoder_hid_proj = ImageProjection(
                image_embed_dim=encoder_hid_dim,
                cross_attention_dim=cross_attention_dim,
            )
        elif encoder_hid_dim_type is not None:
            raise ValueError(
                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
            )
        else:
            self.encoder_hid_proj = None

    def _set_class_embedding(
        self,
        class_embed_type: Optional[str],
        act_fn: str,
        num_class_embeds: Optional[int],
        projection_class_embeddings_input_dim: Optional[int],
        time_embed_dim: int,
        timestep_input_dim: int,
    ):
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        elif class_embed_type == "projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
                )
            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
            # 2. it projects from an arbitrary input dimension.
            #
            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
        elif class_embed_type == "simple_projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
                )
            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
        else:
            self.class_embedding = None
    
    def _set_add_embedding(
        self,
        addition_embed_type: str,
        addition_embed_type_num_heads: int,
        addition_time_embed_dim: Optional[int],
        flip_sin_to_cos: bool,
        freq_shift: float,
        cross_attention_dim: Optional[int],
        encoder_hid_dim: Optional[int],
        projection_class_embeddings_input_dim: Optional[int],
        time_embed_dim: int,
    ):
        if addition_embed_type == "text":
            if encoder_hid_dim is not None:
                text_time_embedding_from_dim = encoder_hid_dim
            else:
                text_time_embedding_from_dim = cross_attention_dim

            self.add_embedding = TextTimeEmbedding(
                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
            )
        elif addition_embed_type == "text_image":
            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)`
            self.add_embedding = TextImageTimeEmbedding(
                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
            )
        elif addition_embed_type == "text_time":
            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
        elif addition_embed_type == "image":
            # Kandinsky 2.2
            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
        elif addition_embed_type == "image_hint":
            # Kandinsky 2.2 ControlNet
            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
        elif addition_embed_type is not None:
            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")

    def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int):
        if attention_type in ["gated", "gated-text-image"]:
            positive_len = 768
            if isinstance(cross_attention_dim, int):
                positive_len = cross_attention_dim
            elif isinstance(cross_attention_dim, (list, tuple)):
                positive_len = cross_attention_dim[0]

            feature_type = "text-only" if attention_type == "gated" else "text-image"
            self.position_net = GLIGENTextBoundingboxProjection(
                positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
            )
    
    def get_time_embed(
        self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int]
    ) -> Optional[torch.Tensor]:
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])

        t_emb = self.time_proj(timesteps)
        # `Timesteps` does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=sample.dtype)
        return t_emb

    def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
        class_emb = None
        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when num_class_embeds > 0")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

                # `Timesteps` does not contain any weights and will always return f32 tensors
                # there might be better ways to encapsulate this.
                class_labels = class_labels.to(dtype=sample.dtype)

            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
        return class_emb

    def get_aug_embed(
        self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
    ) -> Optional[torch.Tensor]:
        aug_emb = None
        if self.config.addition_embed_type == "text":
            aug_emb = self.add_embedding(encoder_hidden_states)
        elif self.config.addition_embed_type == "text_image":
            # Kandinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
                )

            image_embs = added_cond_kwargs.get("image_embeds")
            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
            aug_emb = self.add_embedding(text_embs, image_embs)
        elif self.config.addition_embed_type == "text_time":
            # SDXL - style
            if "text_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
                )
            text_embeds = added_cond_kwargs.get("text_embeds")
            if "time_ids" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
                )
            time_ids = added_cond_kwargs.get("time_ids")
            time_embeds = self.add_time_proj(time_ids.flatten())
            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
            add_embeds = add_embeds.to(emb.dtype)
            aug_emb = self.add_embedding(add_embeds)
        elif self.config.addition_embed_type == "image":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
                )
            image_embs = added_cond_kwargs.get("image_embeds")
            aug_emb = self.add_embedding(image_embs)
        elif self.config.addition_embed_type == "image_hint":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
                )
            image_embs = added_cond_kwargs.get("image_embeds")
            hint = added_cond_kwargs.get("hint")
            aug_emb = self.add_embedding(image_embs, hint)
        return aug_emb

    def process_encoder_hidden_states(
        self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
    ) -> torch.Tensor:
        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
            # Kandinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )

            image_embeds = added_cond_kwargs.get("image_embeds")
            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )
            image_embeds = added_cond_kwargs.get("image_embeds")
            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )
            image_embeds = added_cond_kwargs.get("image_embeds")
            image_embeds = self.encoder_hid_proj(image_embeds)
            encoder_hidden_states = (encoder_hidden_states, image_embeds)
        return encoder_hidden_states

    def forward(
        self,
        sample: torch.Tensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        mid_block_additional_residual: Optional[torch.Tensor] = None,
        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
        **kwargs
    ) -> Union[UNet2DConditionOutput, Tuple]:
        r"""
        The [`UNet2DConditionModel`] forward method.

        Args:
            sample (`torch.Tensor`):
                The noisy input tensor with the following shape `(batch, channel, height, width)`.
            timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.Tensor`):
                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
                through the `self.time_embedding` layer to obtain the timestep embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            added_cond_kwargs: (`dict`, *optional*):
                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
                are passed along to the UNet blocks.
            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
                A tuple of tensors that if specified are added to the residuals of down unet blocks.
            mid_block_additional_residual: (`torch.Tensor`, *optional*):
                A tensor that if specified is added to the residual of the middle unet block.
            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
            encoder_attention_mask (`torch.Tensor`):
                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
                which adds large negative values to the attention scores corresponding to "discard" tokens.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,
                otherwise a `tuple` is returned where the first element is the sample tensor.
        """
        # By default samples have to be AT least a multiple of the overall upsampling factor.
        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
        # However, the upsampling interpolation output size can be forced to fit any upsampling size
        # on the fly if necessary.
        default_overall_up_factor = 2**self.num_upsamplers

        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
        forward_upsample_size = False
        upsample_size = None

        for dim in sample.shape[-2:]:
            if dim % default_overall_up_factor != 0:
                # Forward upsample size to force interpolation output size.
                forward_upsample_size = True
                break
        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None:
            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        # 0. center input if necessary
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        t_emb = self.get_time_embed(sample=sample, timestep=timestep)
        emb = self.time_embedding(t_emb, timestep_cond)
        aug_emb = None

        class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
        if class_emb is not None:
            if self.config.class_embeddings_concat:
                emb = torch.cat([emb, class_emb], dim=-1)
            else:
                emb = emb + class_emb

        aug_emb = self.get_aug_embed(
            emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )
        if self.config.addition_embed_type == "image_hint":
            aug_emb, hint = aug_emb
            sample = torch.cat([sample, hint], dim=1)

        emb = emb + aug_emb if aug_emb is not None else emb

        if self.time_embed_act is not None:
            emb = self.time_embed_act(emb)

        encoder_hidden_states = self.process_encoder_hidden_states(
            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )

        # 2. pre-process
        sample = self.conv_in(sample)

        # 2.5 GLIGEN position net
        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
            cross_attention_kwargs = cross_attention_kwargs.copy()
            gligen_args = cross_attention_kwargs.pop("gligen")
            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}

        # 3. down
        # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
        # to the internal blocks and will raise deprecation warnings. this will be confusing for our users.
        if cross_attention_kwargs is not None:
            cross_attention_kwargs = cross_attention_kwargs.copy()
            lora_scale = cross_attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

        if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)

        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
        is_adapter = down_intrablock_additional_residuals is not None
        # maintain backward compatibility for legacy usage, where
        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
        #       but can only use one or the other
        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
            deprecate(
                "T2I should not use down_block_additional_residuals",
                "1.3.0",
                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
                standard_warn=False,
            )
            down_intrablock_additional_residuals = down_block_additional_residuals
            is_adapter = True

        down_block_res_samples = (sample,)
        if self.enable_unet_cache:
            step = kwargs.get("step", 0)
            if len(self.cache_step) > 0 and (step + 1) not in self.cache_step:
                for block_id, downsample_block in enumerate(self.down_blocks):
                    if block_id >= 2: # skip last block
                        break
                    if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                        # For t2i-adapter CrossAttnDownBlock2D
                        additional_residuals = {}
                        if is_adapter and len(down_intrablock_additional_residuals) > 0:
                            additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)

                        sample, res_samples = downsample_block(
                            hidden_states=sample,
                            temb=emb,
                            encoder_hidden_states=encoder_hidden_states,
                            attention_mask=attention_mask,
                            cross_attention_kwargs=cross_attention_kwargs,
                            encoder_attention_mask=encoder_attention_mask,
                            **additional_residuals,
                        )
                    else:
                        sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
                        if is_adapter and len(down_intrablock_additional_residuals) > 0:
                            sample += down_intrablock_additional_residuals.pop(0)

                    down_block_res_samples += res_samples

                if is_controlnet:
                    new_down_block_res_samples = ()

                    for down_block_res_sample, down_block_additional_residual in zip(
                        down_block_res_samples, down_block_additional_residuals
                    ):
                        down_block_res_sample = down_block_res_sample + down_block_additional_residual
                        new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)

                    down_block_res_samples = new_down_block_res_samples
                sample = self.cache.detach()
                # 5. up
                for i, upsample_block in enumerate(self.up_blocks):
                    is_final_block = i == len(self.up_blocks) - 1
                    if i == 1:
                        res_samples = down_block_res_samples[-4:-1]
                        if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                            sample = upsample_block(
                                hidden_states=sample,
                                temb=emb,
                                res_hidden_states_tuple=res_samples,
                                encoder_hidden_states=encoder_hidden_states,
                                cross_attention_kwargs=cross_attention_kwargs,
                                upsample_size=upsample_size,
                                attention_mask=attention_mask,
                                encoder_attention_mask=encoder_attention_mask,
                            )
                    if i == 2:
                        res_samples = down_block_res_samples[:3]
                        sample = upsample_block(
                            hidden_states=sample,
                            temb=emb,
                            res_hidden_states_tuple=res_samples,
                            upsample_size=upsample_size,
                        )
            else:
                for downsample_block in self.down_blocks:
                    if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                        # For t2i-adapter CrossAttnDownBlock2D
                        additional_residuals = {}
                        if is_adapter and len(down_intrablock_additional_residuals) > 0:
                            additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)

                        sample, res_samples = downsample_block(
                            hidden_states=sample,
                            temb=emb,
                            encoder_hidden_states=encoder_hidden_states,
                            attention_mask=attention_mask,
                            cross_attention_kwargs=cross_attention_kwargs,
                            encoder_attention_mask=encoder_attention_mask,
                            **additional_residuals,
                        )
                    else:
                        sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
                        if is_adapter and len(down_intrablock_additional_residuals) > 0:
                            sample += down_intrablock_additional_residuals.pop(0)

                    down_block_res_samples += res_samples

                if is_controlnet:
                    new_down_block_res_samples = ()

                    for down_block_res_sample, down_block_additional_residual in zip(
                        down_block_res_samples, down_block_additional_residuals
                    ):
                        down_block_res_sample = down_block_res_sample + down_block_additional_residual
                        new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)

                    down_block_res_samples = new_down_block_res_samples

                # 4. mid
                if self.mid_block is not None:
                    if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                        sample = self.mid_block(
                            sample,
                            emb,
                            encoder_hidden_states=encoder_hidden_states,
                            attention_mask=attention_mask,
                            cross_attention_kwargs=cross_attention_kwargs,
                            encoder_attention_mask=encoder_attention_mask,
                        )
                    else:
                        sample = self.mid_block(sample, emb)

                    # To support T2I-Adapter-XL
                    if (
                        is_adapter
                        and len(down_intrablock_additional_residuals) > 0
                        and sample.shape == down_intrablock_additional_residuals[0].shape
                    ):
                        sample += down_intrablock_additional_residuals.pop(0)

                if is_controlnet:
                    sample = sample + mid_block_additional_residual

                # 5. up
                for i, upsample_block in enumerate(self.up_blocks):
                    is_final_block = i == len(self.up_blocks) - 1

                    res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
                    down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

                    # if we have not reached the final block and need to forward the
                    # upsample size, we do it here
                    if not is_final_block and forward_upsample_size:
                        upsample_size = down_block_res_samples[-1].shape[2:]

                    if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                        sample = upsample_block(
                            hidden_states=sample,
                            temb=emb,
                            res_hidden_states_tuple=res_samples,
                            encoder_hidden_states=encoder_hidden_states,
                            cross_attention_kwargs=cross_attention_kwargs,
                            upsample_size=upsample_size,
                            attention_mask=attention_mask,
                            encoder_attention_mask=encoder_attention_mask,
                        )
                    else:
                        sample = upsample_block(
                            hidden_states=sample,
                            temb=emb,
                            res_hidden_states_tuple=res_samples,
                            upsample_size=upsample_size,
                        )
                    if i == 0:
                        self.cache = sample
        else:
            for downsample_block in self.down_blocks:
                if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                    # For t2i-adapter CrossAttnDownBlock2D
                    additional_residuals = {}
                    if is_adapter and len(down_intrablock_additional_residuals) > 0:
                        additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)

                    sample, res_samples = downsample_block(
                        hidden_states=sample,
                        temb=emb,
                        encoder_hidden_states=encoder_hidden_states,
                        attention_mask=attention_mask,
                        cross_attention_kwargs=cross_attention_kwargs,
                        encoder_attention_mask=encoder_attention_mask,
                        **additional_residuals,
                    )
                else:
                    sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
                    if is_adapter and len(down_intrablock_additional_residuals) > 0:
                        sample += down_intrablock_additional_residuals.pop(0)

                down_block_res_samples += res_samples

            if is_controlnet:
                new_down_block_res_samples = ()

                for down_block_res_sample, down_block_additional_residual in zip(
                    down_block_res_samples, down_block_additional_residuals
                ):
                    down_block_res_sample = down_block_res_sample + down_block_additional_residual
                    new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)

                down_block_res_samples = new_down_block_res_samples

            # 4. mid
            if self.mid_block is not None:
                if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                    sample = self.mid_block(
                        sample,
                        emb,
                        encoder_hidden_states=encoder_hidden_states,
                        attention_mask=attention_mask,
                        cross_attention_kwargs=cross_attention_kwargs,
                        encoder_attention_mask=encoder_attention_mask,
                    )
                else:
                    sample = self.mid_block(sample, emb)

                # To support T2I-Adapter-XL
                if (
                    is_adapter
                    and len(down_intrablock_additional_residuals) > 0
                    and sample.shape == down_intrablock_additional_residuals[0].shape
                ):
                    sample += down_intrablock_additional_residuals.pop(0)

            if is_controlnet:
                sample = sample + mid_block_additional_residual

            # 5. up
            for i, upsample_block in enumerate(self.up_blocks):
                is_final_block = i == len(self.up_blocks) - 1

                res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
                down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

                # if we have not reached the final block and need to forward the
                # upsample size, we do it here
                if not is_final_block and forward_upsample_size:
                    upsample_size = down_block_res_samples[-1].shape[2:]

                if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                    sample = upsample_block(
                        hidden_states=sample,
                        temb=emb,
                        res_hidden_states_tuple=res_samples,
                        encoder_hidden_states=encoder_hidden_states,
                        cross_attention_kwargs=cross_attention_kwargs,
                        upsample_size=upsample_size,
                        attention_mask=attention_mask,
                        encoder_attention_mask=encoder_attention_mask,
                    )
                else:
                    sample = upsample_block(
                        hidden_states=sample,
                        temb=emb,
                        res_hidden_states_tuple=res_samples,
                        upsample_size=upsample_size,
                    )

        # 6. post-process
        if self.conv_norm_out:
            sample = self.conv_norm_out(sample)
            sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

        if not return_dict:
            return (sample,)

        return UNet2DConditionOutput(sample=sample)